MétaCan
Menu
Back to cohort
Record W2996969538

Family medicine residents' training in, knowledge about, and perceptions of digital rectal examination.

2017· article· en· W2996969538 on OpenAlexaffabout
Annick Bussières, Alexandre Bouchard, David Simonyan, Sébastien Drolet

Bibliographic record

VenuePubMed · 2017
Typearticle
Languageen
FieldMedicine
TopicColorectal Cancer Screening and Detection
Canadian institutionsCentre hospitalier universitaire de QuébecUniversité Laval
Fundersnot available
KeywordsRectal examinationMedicineFamily medicineComplaintPerceptionMedical educationPsychology
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVE: To evaluate family medicine residents' training in, knowledge about, and perceptions of digital rectal examination (DRE). DESIGN: Descriptive study, using an online survey that was available in French and English. SETTING: Quebec. PARTICIPANTS: A total of 217 residents enrolled in a family medicine program. MAIN OUTCOME MEASURES: Residents' demographic characteristics; the DRE teaching they received throughout their medical training; their reasons for omitting DRE; their recognition of DRE indications (strong vs weak) and application of DRE for 10 anorectal complaints; and their perceptions of the overall quality of the DRE training they received. RESULTS: Of the 879 residents contacted, 217 (25%) responded to the survey. Throughout their training, one-third of respondents did not receive any supervision for or feedback on DRE technique. Seventy-one percent of respondents expressed their inability to identify the nature of abnormal examination findings at least once during their training. The most frequently reported reasons to omit DRE were patient refusal, inadequate setting, and lack of time. CONCLUSION: Most of the residents in this study had omitted DRE at least once in their clinical work despite recognizing its importance. There was discordance between recognition of a complaint requiring DRE and execution of this technique in a clinical setting. Family medicine education programs and continuing medical education committees should consider including DRE training.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.894
Threshold uncertainty score0.291

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.060
GPT teacher head0.307
Teacher spread0.247 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations9
Published2017
Admission routes2
Has abstractyes

Explore more

Same venuePubMedSame topicColorectal Cancer Screening and DetectionFrench-language works237,207